The invention relates generally to non-invasive imaging. More particularly, the invention relates to methods and systems for targeted iterative reconstruction for use in non-invasive imaging.
In the fields of medical imaging and security screening, non-invasive imaging techniques have gained importance due to benefits that include unobtrusiveness, ease, and speed. In medical and research contexts, these imaging systems are used to image organs or tissues beneath the surface of the skin. A number of non-invasive imaging modalities exist today. A particular modality may be selected based upon the organ or tissue to be imaged, upon the spatial and/or temporal resolution desired, or upon whether structural or functional characteristics are of interest. Certain of these non-invasive imaging modalities collect tomographic data that includes sets of line integrals from multiple directions. Examples of these imaging modalities include X-ray computed tomography (CT) imaging, positron emission tomography (PET) imaging, single photon emission computed tomography (SPECT) imaging, magnetic resonance imaging (MRI) using projection reconstruction, and X-ray tomosynthesis.
Certain applications of these imaging modalities require high-resolution images of a targeted field of view (FOV) that is less than the full scan FOV of the imaging system. For example, in cardiac imaging, a high-resolution image of a small sub-region of the patient's anatomy may be desired. However, in X-ray tomography, reconstruction of the measured projection data may rely on measured projection data from outside the targeted FOV. While reconstruction of this targeted FOV is generally straightforward for analytical reconstruction algorithms (such as filtered back projection), iterative reconstruction techniques typically consider the targeted FOV and the regions of the full scan FOV that surround the targeted FOV. This is because iterative reconstruction techniques attempt to match the estimated projection data (derived from forward projection of an estimated image) to the measured projection data. However, if the estimated projection data do not support the signal from outside the targeted FOV, the estimated projection data cannot correctly match the measured projection data.
In general, the signal from outside the targeted FOV should be accounted for in the image reconstruction. If the signal from outside the targeted FOV is not accounted for, the entire signal from outside the targeted FOV may be assigned to the periphery of the targeted FOV or may produce aliasing artifacts inside the targeted FOV. This approach may result in a visible artifact at the periphery of the reconstructed image and quantitatively inaccurate regions throughout the reconstructed image. In other cases, when a targeted FOV less than the scan FOV is requested, the full scan FOV may be reconstructed at high resolution. Subsequently, the image for desired targeted FOV may be extracted from this image for the full scan FOV. This approach, however, reconstructs an image for a full pixel grid (e.g., a full scan) even though only a partial pixel grid for the targeted FOV was requested. As the computational time and image storage requirements grow significantly based on the number of pixels in the reconstruction, this approach may be computationally expensive.